arXiv subjects

Andreas Hofmann

Publications and source records attributed to Andreas Hofmann.

At least 19 recordsLinked to original sources

arXiv2025

A 3D Printed Quad-Ridged Flared Horn Antenna Feeder for Radio-Telescopes

This study proposes a 3D-printed, quad-ridged, dual-pol, flared horn antenna feeder to meet the demands of a modern radio-telescope. The research work presented in this paper involves both the theoretical and the customized / adapted mechanical design of the proposed horn antenna, as well as a description of the 3D-printing and coating process. A novel slotted waveguide approach is followed for perfectly homogeneous coating. Additional mechanical adaptions (i.e. optimized feed section, radome) are employed to provide realistic simulation results. The proposed antenna offers a dual polarization capability and an attractive radiation beam to properly illuminate a 6m mesh dish reflector. A satisfying impedance matching from 1-3 GHz is achieved in both polarizations while maintaining an excellent isolation between the two N-type ports. Reflection coefficient and radiation pattern simulation results are presented, derived from the theoretical and the adapted mechanical design, exhibiting a close agreement.

astro-ph.IM↗

arXiv2024

Reconfigurable Intelligent Surface for Industrial Automation: mmWave Propagation Measurement, Simulation, and Control Algorithm Requirements

Reconfigurable intelligent surfaces (RISs) enable reliable low-latency millimeter wave (mmWave) communication links in cases of a blocked line-of-sight (LoS) between the base station (BS) and the user equipment (UE), i.e. a RIS mounted on a wall or the ceiling provides a bypass for the radio communication link. We present an active RIS with 127 patch antenna elements arranged in a hexagonal grid for a center frequency of 23.8 GHz. Each RIS element uses an orthogonal polarization transformation to enable amplification using a field-effect transistor (FET). The source and drain voltages of each FET is controlled using two bits. We assume that the coordinates of the UE in an industrial control scenario are known to the RIS. We measure the received power on a 2D grid of 60 cm by 100 cm with the RIS working in reflective and active mode. The results show that the RIS can successfully focus the radio signal at the desired target points. The half-power beam width is characterized in axial and radial directions with respect to the RIS position, obtaining a practical RIS configuration update criterion for a mobile UE. These results clearly show that RISs are prominent solutions for enabling reliable wireless communication in indoor industrial scenarios.

eess.SY↗

arXiv2023

Active Reconfigurable Intelligent Surface for the Millimeter-Wave Frequency Band: Design and Measurement Results

Reconfigurable intelligent surfaces (RISs) will play a key role to establish reliable low-latency millimeter wave (mmWave) communication links for indoor automation and control applications. In case of a blocked line-of-sight between the base station (BS) and the user equipment (UE), a RIS mounted on a wall or on a ceiling enables a bypass for the radio communication link. In this work, we present an active RIS for the mmWave frequency band. Each RIS element uses a field effect transistor (FET) to amplify the reflected signal and an orthogonal polarization transformation to increase the isolation between impinging and reflected radio wave. By switching the bias voltage at gate and drain of the FET we can establish four states for each RIS element: two reflection states with different phase shifts, an active amplification and an off state. We present results of the active RIS with 37 patch antenna elements arranged in a hexagonal grid for a center frequency of 25.8 GHz. The RIS field patterns obtained by numerical simulations and by empirical measurements in an anechoic chamber are compared. They show a good match and the received power is improved by 12 dB in the active mode of the RIS compared to the reflective mode.

eess.SY↗

arXiv2023

Chance-Constrained Trajectory Optimization for High-DOF Robots in Uncertain Environments

Many practical applications of robotics require systems that can operate safely despite uncertainty. In the context of motion planning, two types of uncertainty are particularly important when planning safe robot trajectories. The first is environmental uncertainty -- uncertainty in the locations of nearby obstacles, stemming from sensor noise or (in the case of obstacles' future locations) prediction error. The second class of uncertainty is uncertainty in the robots own state, typically caused by tracking or estimation error. To achieve high levels of safety, it is necessary for robots to consider both of these sources of uncertainty. In this paper, we propose a risk-bounded trajectory optimization algorithm, known as Sequential Convex Optimization with Risk Optimization (SCORA), to solve chance-constrained motion planning problems despite both environmental uncertainty and tracking error. Through experiments in simulation, we demonstrate that SCORA significantly outperforms state-of-the-art risk-aware motion planners both in planning time and in the safety of the resulting trajectories.

cs.RO↗

arXiv2022

Cooperative Task and Motion Planning for Multi-Arm Assembly Systems

Multi-robot assembly systems are becoming increasingly appealing in manufacturing due to their ability to automatically, flexibly, and quickly construct desired structural designs. However, effectively planning for these systems in a manner that ensures each robot is simultaneously productive, and not idle, is challenging due to (1) the close proximity that the robots must operate in to manipulate the structure and (2) the inherent structural partial orderings on when each part can be installed. In this paper, we present a task and motion planning framework that jointly plans safe, low-makespan plans for a team of robots to assemble complex spatial structures. Our framework takes a hierarchical approach that, at the high level, uses Mixed-integer Linear Programs to compute an abstract plan comprised of an allocation of robots to tasks subject to precedence constraints and, at the low level, builds on a state-of-the-art algorithm for Multi-Agent Path Finding to plan collision-free robot motions that realize this abstract plan. Critical to our approach is the inclusion of certain collision constraints and movement durations during high-level planning, which better informs the search for abstract plans that are likely to be both feasible and low-makespan while keeping the search tractable. We demonstrate our planning system on several challenging assembly domains with several (sometimes heterogeneous) robots with grippers or suction plates for assembling structures with up to 23 objects involving Lego bricks, bars, plates, or irregularly shaped blocks.

cs.RO↗

arXiv2022

Transformation-optics modeling of 3D-printed freeform waveguides

Multi-photon lithography allows to complement planar photonic integrated circuits (PIC) by in-situ 3D-printed freeform waveguide structures. However, design and optimization of such freeform waveguides using time-domain Maxwell's equations solvers often requires comparatively large computational volumes, within which the structure of interest only occupies a small fraction, thus leading to poor computational efficiency. In this paper, we present a solver-independent transformation-optics-(TO-) based technique that allows to greatly reduce the computational effort related to modeling of 3D freeform waveguides. The concept relies on transforming freeform waveguides with curved trajectories into equivalent waveguide structures with modified material properties but geometrically straight trajectories, that can be efficiently fit into rather small cuboid-shaped computational volumes. We demonstrate the viability of the technique and benchmark its performance using a series of different freeform waveguides, achieving a reduction of the simulation time by a factor of 3-6 with a significant potential for further improvement. We also fabricate and experimentally test the simulated waveguides by 3D-printing on a silicon photonic chip, and we find good agreement between the simulated and the measured transmission at $\lambda = 1550 \textrm{ nm}$.

physics.optics↗

arXiv2021

Automatic Curricula via Expert Demonstrations

We propose Automatic Curricula via Expert Demonstrations (ACED), a reinforcement learning (RL) approach that combines the ideas of imitation learning and curriculum learning in order to solve challenging robotic manipulation tasks with sparse reward functions. Curriculum learning solves complicated RL tasks by introducing a sequence of auxiliary tasks with increasing difficulty, yet how to automatically design effective and generalizable curricula remains a challenging research problem. ACED extracts curricula from a small amount of expert demonstration trajectories by dividing demonstrations into sections and initializing training episodes to states sampled from different sections of demonstrations. Through moving the reset states from the end to the beginning of demonstrations as the learning agent improves its performance, ACED not only learns challenging manipulation tasks with unseen initializations and goals, but also discovers novel solutions that are distinct from the demonstrations. In addition, ACED can be naturally combined with other imitation learning methods to utilize expert demonstrations in a more efficient manner, and we show that a combination of ACED with behavior cloning allows pick-and-place tasks to be learned with as few as 1 demonstration and block stacking tasks to be learned with 20 demonstrations.

cs.LG↗

arXiv2020

Fast-reactive probabilistic motion planning for high-dimensional robots

Many real-world robotic operations that involve high-dimensional humanoid robots require fast-reaction to plan disturbances and probabilistic guarantees over collision risks, whereas most probabilistic motion planning approaches developed for car-like robots can not be directly applied to high-dimensional robots. In this paper, we present probabilistic Chekov (p-Chekov), a fast-reactive motion planning system that can provide safety guarantees for high-dimensional robots suffering from process noises and observation noises. Leveraging recent advances in machine learning as well as our previous work in deterministic motion planning that integrated trajectory optimization into a sparse roadmap framework, p-Chekov demonstrates its superiority in terms of collision avoidance ability and planning speed in high-dimensional robotic motion planning tasks in complex environments without the convexification of obstacles. Comprehensive theoretical and empirical analysis provided in this paper shows that p-Chekov can effectively satisfy user-specified chance constraints over collision risk in practical robotic manipulation tasks.

cs.RO↗

arXiv2020

An Empowerment-based Solution to Robotic Manipulation Tasks with Sparse Rewards

In order to provide adaptive and user-friendly solutions to robotic manipulation, it is important that the agent can learn to accomplish tasks even if they are only provided with very sparse instruction signals. To address the issues reinforcement learning algorithms face when task rewards are sparse, this paper proposes an intrinsic motivation approach that can be easily integrated into any standard reinforcement learning algorithm and can allow robotic manipulators to learn useful manipulation skills with only sparse extrinsic rewards. Through integrating and balancing empowerment and curiosity, this approach shows superior performance compared to other state-of-the-art intrinsic exploration approaches during extensive empirical testing. Qualitative analysis also shows that when combined with diversity-driven intrinsic motivations, this approach can help manipulators learn a set of diverse skills which could potentially be applied to other more complicated manipulation tasks and accelerate their learning process.

cs.RO↗

arXiv2020

Fast Certification of Collision Probability Bounds with Uncertain Convex Obstacles

To operate reactively in uncertain environments, robots need to be able to quickly estimate the risk that they will collide with their environment. This ability is important for both planning (to ensure that plans maintain acceptable levels of safety) and execution (to provide real-time warnings when risk exceeds some threshold). Existing methods for estimating this risk are often limited to models with simplified geometry (e.g. point robots); others handle complex geometry but are too slow for many applications. In this paper, we present two algorithms for quickly computing upper bounds on the risk of collision between a robot and uncertain obstacles by searching for certificate regions that capture collision probability mass while avoiding the robot. These algorithms come with strong theoretical guarantees that the true risk does not exceed the estimated value, support arbitrary geometry via convex decomposition, and provide fast query times ($<200μ$s) in representative scenarios. We characterize the performance of these algorithms in environments of varying complexity, demonstrating at least an order of magnitude speedup over existing techniques.

cs.RO↗

arXiv2020

Provably Safe Trajectory Optimization in the Presence of Uncertain Convex Obstacles

Real-world environments are inherently uncertain, and to operate safely in these environments robots must be able to plan around this uncertainty. In the context of motion planning, we desire systems that can maintain an acceptable level of safety as the robot moves, even when the exact locations of nearby obstacles are not known. In this paper, we solve this chance-constrained motion planning problem using a sequential convex optimization framework. To constrain the risk of collision incurred by planned movements, we employ geometric objects called $ε$-shadows to compute upper bounds on the risk of collision between the robot and uncertain obstacles. We use these $ε$-shadow-based estimates as constraints in a nonlinear trajectory optimization problem, which we then solve by iteratively linearizing the non-convex risk constraints. This sequential optimization approach quickly finds trajectories that accomplish the desired motion while maintaining a user-specified limit on collision risk. Our method can be applied to robots and environments with arbitrary convex geometry; even in complex environments, it runs in less than a second and provides provable guarantees on the safety of planned trajectories, enabling fast, reactive, and safe robot motion in realistic environments.

cs.RO↗

arXiv2019

Hardwire-Configurable Photonic Integrated Circuits Enabled by 3D Nano-Printing

Photonic integrated circuits (PIC) currently rely on application-specific designs that are geared towards a particular functionality. Development of such application-specific PIC requires considerable effort and often involves several design, fabrication, and characterization iterations, each with cycle times of several months, to reach acceptable performance. At the same time, the number of chips that is eventually needed to serve a certain application is often too small to benefit from the scalability of advanced photonic integration platforms. As a consequence, large-scale photonic integration has hitherto been unable to unfold its full economic advantages within the highly fragmented application space of photonics. Here we introduce a novel approach to configurable PIC that can overcome these challenges. The concept exploits generic PIC designs consisting of standard building blocks that can be concatenated to provide an application-specific functionality. Configuration of the PIC relies on establishing hardwired optical connections between suitable ports of an optical wire board using highly flexible 3D direct-write laser lithography. Hardwire-configurable PIC allow to exploit high-throughput production of generic chips in large-scale fabrication facilities for serving a wide variety of applications with small or medium volumes of specifically configured devices.

physics.app-ph↗

arXiv2019

Domain Boundary Detection in Hi-C Maps: A Probabilistic Graphical Model Approach

To understand the nature of a cell, one needs to understand the structure of its genome. For this purpose, experimental techniques such as Hi-C detecting chromosomal contacts are used to probe the three-dimensional genomic structure. These experiments yield topological information, consistently showing a hierarchical subdivision of the genome into self-interacting domains across many organisms. Current methods for detecting these domains using the Hi-C contact matrix, i.e. a doubly-stochastic matrix, are mostly based on the assumption that the domains are distinct, thus non-overlapping. For overcoming this simplification and for being able to unravel a possible nested domain structure, we developed a probabilistic graphical model that makes no a priori assumptions on the domain structure. Within this approach, the Hi-C contact matrix is analyzed using an Ising like probabilistic graphical model whose coupling constant is proportional to each lattice point (entry in the contact matrix). The results show clear boundaries between identified domains and the background. These domain boundaries are dependent on the coupling constant, so that one matrix yields several clusters of different sizes, which show the self-interaction of the genome on different scales.

q-bio.QM↗

arXiv2019

Online Risk-Bounded Motion Planning for Autonomous Vehicles in Dynamic Environments

A crucial challenge to efficient and robust motion planning for autonomous vehicles is understanding the intentions of the surrounding agents. Ignoring the intentions of the other agents in dynamic environments can lead to risky or over-conservative plans. In this work, we model the motion planning problem as a partially observable Markov decision process (POMDP) and propose an online system that combines an intent recognition algorithm and a POMDP solver to generate risk-bounded plans for the ego vehicle navigating with a number of dynamic agent vehicles. The intent recognition algorithm predicts the probabilistic hybrid motion states of each agent vehicle over a finite horizon using Bayesian filtering and a library of pre-learned maneuver motion models. We update the POMDP model with the intent recognition results in real time and solve it using a heuristic search algorithm which produces policies with upper-bound guarantees on the probability of near colliding with other dynamic agents. We demonstrate that our system is able to generate better motion plans in terms of efficiency and safety in a number of challenging environments including unprotected intersection left turns and lane changes as compared to the baseline methods.

cs.RO↗

arXiv2019

Hybrid 2D/3D photonic integration for non-planar circuit topologies

Complex photonic integrated circuits (PIC) may have strongly non-planar topologies that require waveguide crossings (WGX) when realized in single-layer integration platforms. The number of WGX increases rapidly with the complexity of the circuit, in particular when it comes to highly interconnected optical switch topologies. Here, we present a concept for WGX-free PIC that rely on 3D-printed freeform waveguide overpasses (WOP). We experimentally demonstrate the viability of our approach using the example of a $4 \times 4$ switch-and-select (SAS) circuit realized on the silicon photonic platform. We further present a comprehensive graph-theoretical analysis of different $n \times n$ SAS circuit topologies. We find that for increasing port counts $n$ of the SAS circuit, the number of WGX increases with $n^4$, whereas the number of WOP increases only in proportion to $n^2$.

cs.ET↗

arXiv2018

Chance Constrained Motion Planning for High-Dimensional Robots

This paper introduces Probabilistic Chekov (p-Chekov), a chance-constrained motion planning system that can be applied to high degree-of-freedom (DOF) robots under motion uncertainty and imperfect state information. Given process and observation noise models, it can find feasible trajectories which satisfy a user-specified bound over the probability of collision. Leveraging our previous work in deterministic motion planning which integrated trajectory optimization into a sparse roadmap framework, p-Chekov shows superiority in its planning speed for high-dimensional tasks. P-Chekov incorporates a linear-quadratic Gaussian motion planning approach into the estimation of the robot state probability distribution, applies quadrature theories to waypoint collision risk estimation, and adapts risk allocation approaches to assign allowable probabilities of failure among waypoints. Unlike other existing risk-aware planners, p-Chekov can be applied to high-DOF robotic planning tasks without the convexification of the environment. The experiment results in this paper show that this p-Chekov system can effectively reduce collision risk and satisfy user-specified chance constraints in typical real-world planning scenarios for high-DOF robots.

cs.RO↗

arXiv2018

Improving Trajectory Optimization using a Roadmap Framework

We present an evaluation of several representative sampling-based and optimization-based motion planners, and then introduce an integrated motion planning system which incorporates recent advances in trajectory optimization into a sparse roadmap framework. Through experiments in 4 common application scenarios with 5000 test cases each, we show that optimization-based or sampling-based planners alone are not effective for realistic problems where fast planning times are required. To the best of our knowledge, this is the first work that presents such a systematic and comprehensive evaluation of state-of-the-art motion planners, which are based on a significant amount of experiments. We then combine different stand-alone planners with trajectory optimization. The results show that the combination of our sparse roadmap and trajectory optimization provides superior performance over other standard sampling-based planners combinations. By using a multi-query roadmap instead of generating completely new trajectories for each planning problem, our approach allows for extensions such as persistent control policy information associated with a trajectory across planning problems. Also, the sub-optimality resulting from the sparsity of roadmap, as well as the unexpected disturbances from the environment, can both be overcome by the real-time trajectory optimization process.

cs.RO↗

arXiv2018

Moment-Sum-Of-Squares Approach For Fast Risk Estimation In Uncertain Environments

In this paper, we address the risk estimation problem where one aims at estimating the probability of violation of safety constraints for a robot in the presence of bounded uncertainties with arbitrary probability distributions. In this problem, an unsafe set is described by level sets of polynomials that is, in general, a non-convex set. Uncertainty arises due to the probabilistic parameters of the unsafe set and probabilistic states of the robot. To solve this problem, we use a moment-based representation of probability distributions. We describe upper and lower bounds of the risk in terms of a linear weighted sum of the moments. Weights are coefficients of a univariate Chebyshev polynomial obtained by solving a sum-of-squares optimization problem in the offline step. Hence, given a finite number of moments of probability distributions, risk can be estimated in real-time. We demonstrate the performance of the provided approach by solving probabilistic collision checking problems where we aim to find the probability of collision of a robot with a non-convex obstacle in the presence of probabilistic uncertainties in the location of the robot and size, location, and geometry of the obstacle.

math.OC↗